World population growth through time

A few months ago I have made an attempt to visualize the world population changes from 1800 to 2100: Inspired by @MaxCRoser and @jkottke, I've tried to visualize the world population changes from 1800 to 2100. My new blog post at https://t.co/XpBpkZLO9s describes how this animation was made using #rstats and #OpenData. pic.twitter.com/WI3gj0xUwU — Jakub Nowosad (@jakub_nowosad) October 9, 2018 This way of visualization is good to show the ever-changing distribution of the population on a global scale.

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Efficient landscape metrics calculations for buffers around sampling points

Landscape metrics are algorithms that quantify physical characteristics of landscape mosaics (aka categorical raster) in order to connect them to some ecological processes. Many different landscape metrics exist and they can provide three main levels of information: (i) landscape level, (ii) class level, and (iii) patch level. A landscape level metric gives just one value describing a certain property of a landscape mosaic, such as its diversity. A class level metric returns one value for each class (category) present in a landscape.

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GeoPAT2: Entropy calculations for local landscapes

Introduction GeoPAT 2 is an open-source software written in C and dedicated to pattern-based spatial and temporal analysis. Four main types of analysis available in GeoPAT 2 are (i) search, (ii) change detection, (iii) segmentation, and (iv) clustering. However, additional applications are also possible, including extracting information about spatial patterns. Global landscape diversity (based on Shannon entropy of land cover categories in a 9 by 9 km local window). It shows where only one land cover category dominates, and where there is a large number of lc categories.

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Geocomputation with R - the afterword

I am extremely proud to announce that Geocomputation with R is complete. It took Robin, Jannes, and me almost 2 years of collaborative planning, writing, refinement, and deployment to make the book available for anyone interested in open source, command-line approaches for handling geographic data. We’re very happy that it’s now ready to present to the world (and that physical copies of the book can be pre-ordered already from Amazon, Wordery and direct from the publisher, CRC Press).

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How much our maps are distorted?

Last week Neil Kaye tweeted: Animating the Mercator projection to the true size of each country in relation to all the others. Focusing on a single country helps to see effect best.#dataviz #maps #GIS #projectionmapping #mapping pic.twitter.com/clpCiluS1z — Neil Kaye (@neilrkaye) October 12, 2018 This, of course, provoked me to ask: is it reproduclible? And more specifically, can it be reproduced in the open source statistical programming language, R?

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The world (population) is changing

Last month, Max Roser presented a cartogram of the Earth’s population in 2018. He also provided some perspectives on its spatial distribution in an article on the worldinourdata.org, which I recommend. Links to the article were shared in many places, including in the blog post A Map of the World Where the Sizes of Countries Are Determined by Population. The author, Jason Kottke, concluded with a wish: “I would love to see an animated version of this cartogram from like 1950 to 2100”.

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sabre: or how to compare two maps?

Creating or determination of regions is a useful way to describe the world. Regionalization does not only allow for a quicker understanding of spatial patterns but also can influence how regions are managed. Regions are created in various disciplines. We can delineate regions based on a single property (e.g. landform regions or climate regions) or several factors (e.g. ecoregions). There are also political regions divided by borders that are established through political or social agreements.

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Moving beyond pattern-based analysis: Additional applications of GeoPAT 2

This is the sixth and the last blog post in the series introducing GeoPAT 2 - a software for pattern-based spatial and temporal analysis. In the previous one we presented the pattern-based spatial segmentation - a method for creating regions of homogenous patterns. Here, we will mention other pattern-based methods and show some examples of how you can use pieces of GeoPAT 2 in your own workflow. Introduction GeoPAT 2 gives its users a lot of freedom, having a large number of possible workflows:

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Life (expectancy), animated

Global socio-economic data is easily accessible nowadays. Just type the indicator of interest and the name of the country in your preferred search engine and you can find its value, sometimes also an additional plot or a map. But what about when you want to go further and (for example): Want to compare many countries? Get data just for a specific year? See changes in time? Just want to create a very specific plot or a map?

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